{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "W5_Tutorial2.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvoa11a7eS91"
      },
      "source": [
        "# CIS 522 Week 5: Regularization\n",
        "\n",
        "\n",
        "__Instructor:__ Lyle Ungar\n",
        "\n",
        "__Content creators:__ Ravi Teja Konkimalla, Mohitrajhu Lingan Kumaraian"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oI04YQ_2IVm8"
      },
      "source": [
        "#### Ensure you're running a GPU notebook.\n",
        "\n",
        "From \"Runtime\" in the drop-down menu above, click \"Change runtime type\". Ensure that \"Hardware Accelerator\" says \"GPU\".\n",
        "\n",
        "#### Ensure you can save!\n",
        "\n",
        "From \"File\", click \"Save a copy in Drive\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QcARZs7tbHIi",
        "cellView": "form"
      },
      "source": [
        "#@title Import Libraries\n",
        "from __future__ import print_function\n",
        "import torch\n",
        "import pathlib\n",
        "import random\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "from torchvision.datasets import ImageFolder\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "import torch.nn.utils.prune as prune\n",
        "from torch.optim.lr_scheduler import StepLR\n",
        "import time\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as animation\n",
        "import copy\n",
        "from tqdm import tqdm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OLIyaJBeIDs1",
        "cellView": "form"
      },
      "source": [
        "#@markdown What is your Pennkey and pod? (text, not numbers, e.g. bfranklin)\n",
        "my_pennkey = '' #@param {type:\"string\"}\n",
        "my_pod = 'Select' #@param ['Select', 'euclidean-wombat', 'sublime-newt', 'buoyant-unicorn', 'lackadaisical-manatee','indelible-stingray','superfluous-lyrebird','discreet-reindeer','quizzical-goldfish','astute-jellyfish','ubiquitous-cheetah','nonchalant-crocodile','fashionable-lemur','spiffy-eagle','electric-emu','quotidian-lion']\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LU2phW9PrXnP"
      },
      "source": [
        "#Question of the Week\n",
        "Why does it work better to regularize an overparameterized ANN than to start with a smaller one? [think about  the regularization  methods you know]\n",
        "Each group has a 10 min discussion."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6QSIMHQhr0T9",
        "cellView": "form"
      },
      "source": [
        "#@markdown Summerize your discussion\n",
        "question_of_the_week = '' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GvXxa4b2l4m4"
      },
      "source": [
        "# Setup\n",
        "Note that some of the code for today can take up to an hour to run. We have therefore \"hidden\" that code and shown the resulting outputs.\n",
        "\n",
        "[Here](https://docs.google.com/presentation/d/1n4eA5VGG8ab0mkW1kJK5egaldJR4cnpFAHDVbkVPnRI/edit#slide=id.gb88533964a_0_198) are the slides for today's videos (in case you want to take notes). **Do not read them now.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDBtaMET-fNA",
        "cellView": "form"
      },
      "source": [
        "# @title Figure Settings\n",
        "import ipywidgets as widgets\n",
        "%matplotlib inline \n",
        "fig_w, fig_h = (8, 6)\n",
        "plt.rcParams.update({'figure.figsize': (fig_w, fig_h)})\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "SMALL_SIZE = 12\n",
        "\n",
        "plt.rcParams.update(plt.rcParamsDefault)\n",
        "plt.rc('animation', html='jshtml')\n",
        "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
        "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
        "plt.rc('axes', labelsize=SMALL_SIZE)    # fontsize of the x and y labels\n",
        "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
        "plt.rc('figure', titlesize=SMALL_SIZE)  # fontsize of the figure title"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvhx88j7e6m2",
        "cellView": "form"
      },
      "source": [
        "# @title Loading Animal Faces data\n",
        "%%capture\n",
        "!rm -r AnimalFaces32x32/\n",
        "!git clone https://github.com/arashash/AnimalFaces32x32\n",
        "!rm -r afhq/\n",
        "!unzip ./AnimalFaces32x32/afhq_32x32.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RMuvvw3VHm1Z",
        "cellView": "form"
      },
      "source": [
        "#@title Seeding for Reproducibility\n",
        "seed = 90108\n",
        "random.seed(seed)\n",
        "np.random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "torch.cuda.manual_seed(seed)\n",
        "torch.cuda.manual_seed_all(seed)\n",
        "torch.backends.cudnn.deterministic = True\n",
        "torch.backends.cudnn.benchmark = True\n",
        "torch.backends.cudnn.enabled = True\n",
        "torch.set_deterministic(True)\n",
        "def seed_worker(worker_id):\n",
        "    worker_seed = seed % (worker_id+1)\n",
        "    np.random.seed(worker_seed)\n",
        "    random.seed(worker_seed)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxCVV-2yt-GX",
        "cellView": "form"
      },
      "source": [
        "# @title Helper functions\r\n",
        "def imshow(img):\r\n",
        "    img = img / 2 + 0.5\r\n",
        "    npimg = img.numpy()\r\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\r\n",
        "    plt.axis(False)\r\n",
        "    plt.show()\r\n",
        "\r\n",
        "def train(args, model, device, train_loader, optimizer, epoch,reg_function1=None,reg_function2=None,criterion=F.nll_loss):\r\n",
        "    model.train()\r\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\r\n",
        "        data, target = data.to(device), target.to(device)\r\n",
        "        optimizer.zero_grad()\r\n",
        "        output = model(data)\r\n",
        "        if reg_function1 is None:\r\n",
        "          loss = criterion(output, target)\r\n",
        "        elif reg_function2 is None:\r\n",
        "          loss = criterion(output, target)+args['lambda']*reg_function1(model)\r\n",
        "        else:\r\n",
        "          loss = criterion(output, target)+args['lambda1']*reg_function1(model)+args['lambda2']*reg_function2(model)\r\n",
        "        loss.backward()\r\n",
        "        optimizer.step()\r\n",
        "        # if (batch_idx % args['log_interval'] == 0 and batch_idx != 0):\r\n",
        "            # print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\r\n",
        "                # epoch, batch_idx * len(data), len(train_loader.dataset),\r\n",
        "                # 100. * batch_idx / len(train_loader), loss.item()))\r\n",
        "\r\n",
        "def test(model, device, test_loader, loader = 'Test',criterion=F.nll_loss):\r\n",
        "    model.eval()\r\n",
        "    test_loss = 0\r\n",
        "    correct = 0\r\n",
        "    with torch.no_grad():\r\n",
        "        for data, target in test_loader:\r\n",
        "            data, target = data.to(device), target.to(device)\r\n",
        "            output = model(data)\r\n",
        "            test_loss += criterion(output, target, reduction='sum').item()  # sum up batch loss\r\n",
        "            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\r\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\r\n",
        "\r\n",
        "    test_loss /= len(test_loader.dataset)\r\n",
        "\r\n",
        "    # print('\\n{} set: Average loss: {:.4f}, Accuracy: {}/{} ({:.4f}%)\\n'.format(\r\n",
        "        # loader, test_loss, correct, len(test_loader.dataset),\r\n",
        "        # 100. * correct / len(test_loader.dataset)))\r\n",
        "    return 100. * correct / len(test_loader.dataset)\r\n",
        "\r\n",
        "def main(args, model,train_loader,val_loader,test_data,reg_function1=None,reg_function2=None,criterion=F.nll_loss):\r\n",
        "  \"\"\"\r\n",
        "  Trains the model with train_loader and tests the learned model using val_loader\r\n",
        "  \"\"\"\r\n",
        "\r\n",
        "  use_cuda = not args['no_cuda'] and torch.cuda.is_available()\r\n",
        "  device = torch.device('cuda' if use_cuda else 'cpu') \r\n",
        "\r\n",
        "  model = model.to(device)\r\n",
        "  optimizer = optim.SGD(model.parameters(), lr=args['lr'], momentum=args['momentum'])\r\n",
        "\r\n",
        "  best_acc  = 0.0\r\n",
        "  best_epoch = 0\r\n",
        "\r\n",
        "  val_acc_list, train_acc_list,param_norm_list = [], [], []\r\n",
        "  for epoch in tqdm(range(args['epochs'])):\r\n",
        "      train(args, model, device, train_loader, optimizer, epoch,reg_function1=reg_function1,reg_function2=reg_function2)\r\n",
        "      train_acc = test(model,device,train_loader, 'Train')\r\n",
        "      val_acc = test(model,device,val_loader, 'Val')\r\n",
        "      param_norm = calculate_frobenius_norm(model)\r\n",
        "      train_acc_list.append(train_acc)\r\n",
        "      val_acc_list.append(val_acc)\r\n",
        "      param_norm_list.append(param_norm)\r\n",
        "\r\n",
        "  return val_acc_list, train_acc_list, param_norm_list, model, best_epoch\r\n",
        "\r\n",
        "def calculate_frobenius_norm(model):\r\n",
        "    norm = 0.0\r\n",
        "\r\n",
        "    for name,param in model.named_parameters():\r\n",
        "        norm += torch.norm(param).data**2\r\n",
        "    return norm**0.5\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSfEJun00dwZ",
        "cellView": "form"
      },
      "source": [
        "# @title Network Classes for Animal Faces\n",
        "class Animal_Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Animal_Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(3*32*32, 128)\n",
        "        self.fc2 = nn.Linear(128, 32)\n",
        "        self.fc3 = nn.Linear(32, 3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(x.shape[0],-1)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        output = F.log_softmax(x, dim=1)\n",
        "        return output\n",
        "\n",
        "\n",
        "class Big_Animal_Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        torch.manual_seed(104)\n",
        "        super(Big_Animal_Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(3*32*32, 124)\n",
        "        self.fc2 = nn.Linear(124, 64)\n",
        "        self.fc3 = nn.Linear(64, 3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(x.shape[0],-1)\n",
        "        x = F.leaky_relu(self.fc1(x))\n",
        "        x = F.leaky_relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        output = F.log_softmax(x, dim=1)\n",
        "        return output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QGBBuMD3vSvT",
        "cellView": "form"
      },
      "source": [
        "# @title Dataloder\r\n",
        "batch_size = 128\r\n",
        "classes = ('cat', 'dog', 'wild')\r\n",
        "\r\n",
        "train_transform = transforms.Compose([\r\n",
        "     transforms.RandomRotation(10),\r\n",
        "     transforms.RandomHorizontalFlip(),\r\n",
        "     transforms.ToTensor(),\r\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  \r\n",
        "     ])\r\n",
        "\r\n",
        "data_path = pathlib.Path('.')/'afhq' # using pathlib to be compatible with all OS's\r\n",
        "img_dataset = ImageFolder(data_path/'train', transform=train_transform)\r\n",
        "img_train_data, img_val_data,_ = torch.utils.data.random_split(img_dataset, [100,100,14430])\r\n",
        "\r\n",
        "train_loader = torch.utils.data.DataLoader(img_train_data,batch_size=batch_size,worker_init_fn=seed_worker)\r\n",
        "val_loader = torch.utils.data.DataLoader(img_val_data,batch_size=1000,worker_init_fn=seed_worker)\r\n",
        "\r\n",
        "test_transform = transforms.Compose([\r\n",
        "     transforms.ToTensor(),\r\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\r\n",
        "     ])\r\n",
        "img_test_dataset = ImageFolder(data_path/'val', transform=test_transform)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u4yoYTBYtQ76"
      },
      "source": [
        "#Section 0: Hyper Parameter Tuning\n",
        "(15 min from start)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "L6iiQE2zt77F"
      },
      "source": [
        "#@title Video : Tuning Methods\n",
        "try: t1;\n",
        "except NameError: t1=time.time()\n",
        "\n",
        "from IPython.display import YouTubeVideo\n",
        "video = YouTubeVideo(id=\"-ly5hwbpx-w\", width=854, height=480, fs=1)\n",
        "print(\"Video available at https://youtube.com/watch?v=\" + video.id)\n",
        "\n",
        "video"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "joL4AygZv96J"
      },
      "source": [
        "While completing the kaggle competition as a part of HW3 you would have noticied how difficult and time consuming Hyper-Parameter tuning can be. While hyper parameter tuning is quite laborious it is also a major part of training any Deep Learning model and paramount for incorporating good generalization capabilities. There are a few techniques that we can use to guide us during the search. \n",
        "\n",
        "\n",
        "\n",
        "*   Grid Search: Try all possible combinations of hyperparameters\n",
        "*   Random Search: Randomly try different combinations of hyperparameters\n",
        "*   Coordinate-wise Gradient Descent: Start at one set of hyperparameters and try changing one at a time, accept any changes that reduce your validation error\n",
        "*   Bayesian Optimization/ Auto ML:  Start from a set of hyperparameters that have worked well on a similar problem, and then do some sort of local exploration (e.g. gradient descent) from there.\n",
        "\n",
        "There are lots of details, like what range to explore over, which parameter to optimize first, etc. Some hyperparameters don’t seem to matter much (people use a dropout of either 0.5 or 0, but not much else).  Others can matter a lot more (e.g. size and depth of the neural net). The key is to see what worked on similar problems.\n",
        "\n",
        "You can automate the process of tuning the network Architecture using Neural Architecture Search which designs new architectures using a few building blocks (Linear, Convolutional, Convolution Layers, etc.) and optimizes the design based on performance using a wide range of techniques such as Grid Search, Reinforcement Learning, GD, Evolutionary Algorithms, etc. This obviously requires very high computer power. Read this [article](https://lilianweng.github.io/lil-log/2020/08/06/neural-architecture-search.html) to learn more about NAS.    \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qaOATxHS8TR"
      },
      "source": [
        "#Section 1: Stochastic Gradient Descent\n",
        "\n",
        "(Time Estimate: 25 min from start)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DV1QFxoRpHm2",
        "cellView": "form"
      },
      "source": [
        "#@title Video : SGD\n",
        "try: t2;\n",
        "except NameError: t2=time.time()\n",
        "\n",
        "from IPython.display import YouTubeVideo\n",
        "video = YouTubeVideo(id=\"E3g2Z-ZqMZw\", width=854, height=480, fs=1)\n",
        "print(\"Video available at https://youtube.com/watch?v=\" + video.id)\n",
        "\n",
        "video"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hxRoCUOaXAXz"
      },
      "source": [
        "## Learning Rate\n",
        "In this section below we will see how learning rate can act as regularizer while training the neural network. In summary:\n",
        "\n",
        "\n",
        "*   Smaller Learning rate does not regularize well. Rather it slowly converges to a local deep minima. \n",
        "*   Larger Learning rate regularizes well by missing local minimas and finding a broader, flatter minima. These minima may be more robust.\n",
        "\n",
        "But beware, taking a very large learning rate may result in overshooting or finding a really bad local minima.\n",
        "\n",
        "\n",
        "\n",
        "In the below block we will train the Animal Net model with different learning rates and see how that is going to affect the regularization performance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sbY2JYF1maP",
        "cellView": "form"
      },
      "source": [
        "#@title Generating Data Loaders\r\n",
        "batch_size = 128\r\n",
        "train_transform = transforms.Compose([\r\n",
        "     transforms.ToTensor(),\r\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))    \r\n",
        "     ])\r\n",
        "\r\n",
        "data_path = pathlib.Path('.')/'afhq' # using pathlib to be compatible with all OS's\r\n",
        "img_dataset = ImageFolder(data_path/'train', transform=train_transform)\r\n",
        "img_train_data, img_val_data, = torch.utils.data.random_split(img_dataset, [11700,2930])\r\n",
        "\r\n",
        "full_train_loader = torch.utils.data.DataLoader(img_train_data,batch_size=batch_size,num_workers=4,worker_init_fn=seed_worker)\r\n",
        "full_val_loader = torch.utils.data.DataLoader(img_val_data,batch_size=1000,num_workers=4,worker_init_fn=seed_worker)\r\n",
        "\r\n",
        "test_transform = transforms.Compose([\r\n",
        "     transforms.ToTensor(),\r\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))    # [TO-DO]\r\n",
        "     ])\r\n",
        "img_test_dataset = ImageFolder(data_path/'val', transform=test_transform)\r\n",
        "# img_test_loader = DataLoader(img_test_dataset, batch_size=batch_size,shuffle=False, num_workers=1)\r\n",
        "classes = ('cat', 'dog', 'wild')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFWmkt9O-047",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e864e61d-9c12-4df4-dba4-ceed0ecfca1a"
      },
      "source": [
        "args = {'test_batch_size': 1000,\n",
        "        'epochs': 350,\n",
        "        'batch_size': 32,\n",
        "        'momentum': 0.99,\n",
        "        'no_cuda': False\n",
        "        }\n",
        "\n",
        "lr = [5e-4,1e-3, 5e-3]\n",
        "acc_dict = {}\n",
        "\n",
        "for i in range(len(lr)):\n",
        "    model = Animal_Net()\n",
        "    args['lr'] = lr[i]\n",
        "    val_acc, train_acc, param_norm,_,_ = main(args,model,train_loader,val_loader,img_test_dataset)\n",
        "    acc_dict['val_'+str(i)] = val_acc\n",
        "    acc_dict['train_'+str(i)] = train_acc\n",
        "    acc_dict['param_norm'+str(i)] = param_norm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 350/350 [00:54<00:00,  6.45it/s]\n",
            "100%|██████████| 350/350 [00:54<00:00,  6.44it/s]\n",
            "100%|██████████| 350/350 [00:54<00:00,  6.39it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80-gI9BJU1Dm",
        "cellView": "form"
      },
      "source": [
        "#@title Plot Train and Validation accuracy (Run me)\n",
        "plt.plot(acc_dict['val_0'], linestyle='dashed',label='lr = 5e-4 - validation', c = 'blue')\n",
        "plt.plot(acc_dict['train_0'],label = '5e-4 - train', c = 'blue')\n",
        "plt.plot(acc_dict['val_1'], linestyle='dashed',label='lr = 1e-3 - validation', c = 'green')\n",
        "plt.plot(acc_dict['train_1'],label='1e-3 - train', c = 'green')\n",
        "plt.plot(acc_dict['val_2'], linestyle='dashed',label='lr = 5e-3 - validation', c = 'purple')\n",
        "plt.plot(acc_dict['train_2'],label = '5e-3 - train', c = 'purple')\n",
        "plt.title('Optimal Learning Rate')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.xlabel('Epoch')\n",
        "print('Maximum Test Accuracy obtained with lr = 5e-4: '+str(max(acc_dict['val_0'])))\n",
        "print('Maximum Test Accuracy obtained with lr = 1e-3: '+str(max(acc_dict['val_1'])))\n",
        "print('Maximum Test Accuracy obtained with lr = 5e-3: '+str(max(acc_dict['val_2'])))\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBYNBuov0rmU",
        "cellView": "form"
      },
      "source": [
        "#@title Plot parametric norms (Run me)\n",
        "plt.plot(acc_dict['param_norm0'],label='lr = 5e-4',c='blue')\n",
        "plt.plot(acc_dict['param_norm1'],label = 'lr = 1e-3',c='green')\n",
        "plt.plot(acc_dict['param_norm2'],label ='lr = 5e-3', c='red')\n",
        "plt.legend()\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('parameter norms')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECKFAMOrN5Fy"
      },
      "source": [
        "In the model above, we observe something different from what we expected. Why do you think this is happening?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uBH50aWCOCnB",
        "cellView": "form"
      },
      "source": [
        "#@markdown Write down the answer\r\n",
        "learning_rate = '' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OySRfKnQGbhW"
      },
      "source": [
        "## Batch Size\n",
        "Batch size, in some cases, can also help in regularizing the models. Lower batch size leads to a noisy convergence and hence helps in converging to a broader local minima. Whereas, higher batch size lead to a smoother convergence thereby converging easily to a  deeper local minima.  This can be good or bad.\n",
        "\n",
        "In the below blcok we will train the Animal Net model with different batch sizes and see how that is going to affect the regularization performance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2aIo8e8NQfWQ",
        "cellView": "form"
      },
      "source": [
        "#@title Dataset for Batch_size\r\n",
        "data_path = pathlib.Path('.')/'afhq' # using pathlib to be compatible with all OS's\r\n",
        "img_dataset = ImageFolder(data_path/'train', transform=train_transform)\r\n",
        "\r\n",
        "#Splitting dataset\r\n",
        "reg_train_data, reg_val_data,_ = torch.utils.data.random_split(img_dataset, [250,100,14280])\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQbO03j8GjUX"
      },
      "source": [
        "args = {'lr': 5e-3,\n",
        "        'epochs': 60,\n",
        "        'momentum': 0.99,\n",
        "        'no_cuda': False\n",
        "        }\n",
        "\n",
        "batch_sizes = [32,64,128]\n",
        "acc_dict = {}\n",
        "\n",
        "for i in range(len(batch_sizes)):\n",
        "    model = Animal_Net()\n",
        "    #Creating train_loader and Val_loader\n",
        "    reg_train_loader = torch.utils.data.DataLoader(reg_train_data,batch_size=batch_sizes[i],worker_init_fn=seed_worker)\n",
        "    reg_val_loader = torch.utils.data.DataLoader(reg_val_data,batch_size=1000,worker_init_fn=seed_worker)\n",
        "    val_acc, train_acc,param_norm,_,_ = main(args,model,reg_train_loader,reg_val_loader,img_test_dataset)\n",
        "    acc_dict['train_'+str(i)] = train_acc\n",
        "    acc_dict['val_'+str(i)] = val_acc\n",
        "    acc_dict['param_norm'+str(i)] = param_norm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ifx1rCTiG-tW",
        "cellView": "form"
      },
      "source": [
        "#@title Plot Train and Val curves\n",
        "plt.plot(acc_dict['train_0'], label='mb_size =' + str(batch_sizes[0]), c = 'blue')\n",
        "plt.plot(acc_dict['val_0'], linestyle='dashed', c = 'blue')\n",
        "\n",
        "plt.plot(acc_dict['train_1'], label='mb_size =' + str(batch_sizes[1]), c = 'orange')\n",
        "plt.plot(acc_dict['val_1'], linestyle='dashed', c = 'orange')\n",
        "plt.plot(acc_dict['train_2'], label='mb_size =' + str(batch_sizes[2]), c = 'green')\n",
        "plt.plot(acc_dict['val_2'], linestyle='dashed', c = 'green')\n",
        "print('maximum accuracy for mini batchsize = 32: '+str(max(acc_dict['val_0'])))\n",
        "print('maximum accuracy for mini batchsize = 64: '+str(max(acc_dict['val_1'])))\n",
        "print('maximum accuracy for mini batchsize = 128: '+str(max(acc_dict['val_2'])))\n",
        "\n",
        "plt.title('Optimal Batch Size')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.xlabel('Epoch (Sec)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6Kp_jf37nnS",
        "cellView": "form"
      },
      "source": [
        "#@title Plot Parametric Norms\r\n",
        "plt.plot(acc_dict['param_norm0'],c='blue',label='mb_size =' + str(batch_sizes[0]))\r\n",
        "plt.plot(acc_dict['param_norm1'],c='orange',label='mb_size =' + str(batch_sizes[1]))\r\n",
        "plt.plot(acc_dict['param_norm2'],c='green',label='mb_size =' + str(batch_sizes[2]))\r\n",
        "plt.xlabel('epoch')\r\n",
        "plt.ylabel('Parameter Norm')\r\n",
        "plt.legend()\r\n",
        "plt.show()\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l_Sbvul0O9AP"
      },
      "source": [
        "Here what observation can you make for different batch size. Why do you think this is happening?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPsOclZ2PIvN",
        "cellView": "form"
      },
      "source": [
        "#@markdown Write down the answer\r\n",
        "batch_size = '' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1dLTAENW0_q"
      },
      "source": [
        "#Section 2: Pruning\n",
        "(Time Estimate: 40 min from start)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZWNq6aknCZQD"
      },
      "source": [
        "Before we dig deeper into pruning let's take a small detour and calculate the inference time (time taken for one forward pass during testing) and number of parameters of the biggest model we trained this week.\n",
        "\n",
        "\n",
        "```\n",
        "class Animal_Net_Dropout(nn.Module):\n",
        "    def __init__(self):\n",
        "        torch.manual_seed(32)\n",
        "        super(Animal_Net_Dropout, self).__init__()\n",
        "        self.fc1 = nn.Linear(3*32*32, 124)\n",
        "        self.fc2 = nn.Linear(124, 64)\n",
        "        self.fc3 = nn.Linear(64, 3)\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bFzHLFwIrUxP"
      },
      "source": [
        "##Exercise 1: Calculating Inference"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWuJ2IKxlUTU"
      },
      "source": [
        "Now calculate by hand the exact total number of parameters and fill in the code below to calculate the average inference time of the above model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mu27sYWFlfM8"
      },
      "source": [
        "def calculate_inference(N):\n",
        "\n",
        "    ####################################################################\n",
        "    # Fill in all missing code below (...),\n",
        "    # then remove or comment the line below to test your function\n",
        "    raise NotImplementedError(\"Define the calculate inference function\")\n",
        "    ####################################################################\n",
        "\n",
        "    total_time = 0.0\n",
        "    model = Big_Animal_Net()\n",
        "    model.eval()\n",
        "    \n",
        "    for i in range(N):\n",
        "        #generate random data of batch 1 which should be passed into the model\n",
        "        X = ....\n",
        "        #make sure you don't calculate gradients to make the compute faster\n",
        "        with ...: \n",
        "            start_time = time.time()\n",
        "            y = ...\n",
        "            end_time = time.time()\n",
        "            total_time ...\n",
        "\n",
        "    print(f'Inference time of the above network is: {total_time/N}')\n",
        "\n",
        "##uncomment to run\n",
        "#calculate_inference(1000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EU-q9txXGCXK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5d64ca2-0d7b-4cbc-cc63-ce4edb3ee472"
      },
      "source": [
        "def calculate_inference(N):\n",
        "\n",
        "    total_time = 0.0\n",
        "    model = Big_Animal_Net()\n",
        "    model.eval()\n",
        "    \n",
        "    for i in range(N):\n",
        "        X = torch.rand((1,3,32,32))\n",
        "        with torch.no_grad():\n",
        "            start_time = time.time()\n",
        "            y = model(X)\n",
        "            end_time = time.time()\n",
        "            total_time += end_time - start_time\n",
        "\n",
        "    print(f'Inference time of the above network is: {total_time/N}')\n",
        "\n",
        "calculate_inference(1000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Inference time of the above network is: 0.0001970212459564209\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76wEp1RzoTIr"
      },
      "source": [
        "[Click for Solution](https://github.com/CIS-522/course-content/blob/main/tutorials/W05_Regularization/solutions/W5_Tutorial2_Ex01.py)\n",
        "\n",
        "Example Output:\n",
        "\n",
        "![Screenshot from 2021-02-12 14-04-49.png]()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZgxj7RiGKsw",
        "cellView": "form"
      },
      "source": [
        "#@markdown Write down the answer\n",
        "number_of_parameters = '' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_IDYTpEHO9c"
      },
      "source": [
        "This relatively small model with approximately 400,000 parameters when trained on 100 training samples with takes about 2 min to finish training with an inference time of approx 3msec,  which can be used for real time applications. But this obviously isn't the best network we can do. We can use a bigger network and train on bigger dataset, infact the entire Animal Faces Dataset consists of about around 15000 training data.Also, we have to keep in mind that the images we are using are of very low resolution(32X32), the sizes of the images in the original dataset is 512X512. While this will definetly improve the performance of the model it will also increase the resources needed to handle the model.\n",
        "\n",
        "Google is known for training very big language models and recently it trained a [trillion paramter](https://thenextweb.com/neural/2021/01/13/googles-new-trillion-parameter-ai-language-model-is-almost-6-times-bigger-than-gpt-3/) model. This is almost 1.2e6 times bigger than the model we just trained. So it is sufficient to say that these big models need intense compute power to train while also becomeing harder to deploy and get real time inference on smaller micro - proccesors. \n",
        "\n",
        "This is where regularization and pruning come in very handy. Until now you should have noticed that the Frobenious norm of the regularized models that we trained tend to be smaller than those of unregualrized models. This indicates that the regualarization is shrinking the weights (making the model sparser) while improving the test performance. \n",
        "\n",
        "While methods like L1 regulartization promote implicit sparsity, in pruning we explicitly set a few weights of the tained model to zero and then retrain the model to adjust the other weights. This reduces the memory consumption, improves inference and helps the planet :)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y5IUWMKh8k_q",
        "cellView": "form"
      },
      "source": [
        "#@title Video :  Pruning\n",
        "try: t3;\n",
        "except NameError: t3=time.time()\n",
        "\n",
        "from IPython.display import YouTubeVideo\n",
        "video = YouTubeVideo(id=\"7S5LA2OFdzs\", width=854, height=480, fs=1)\n",
        "print(\"Video available at https://youtube.com/watch?v=\" + video.id)\n",
        "\n",
        "video"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_eJuNAOiMJuN"
      },
      "source": [
        "One of the most common methods of pruning a NN is to zero out a certain percentage of parameters based on their L1 norm. We don't actually remove the parameters because that makes forward computations difficult.\n",
        "\n",
        "Luckily we have Pytorch's torch.nn.utils.prune methods to play around and test pruning."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Avjo2i_mKDf"
      },
      "source": [
        "##Exercise 2: L1Prune\n",
        "Before we train a pruned model let us write a function to prunes a model. Use [prune.L1Unstructured](https://pytorch.org/docs/stable/generated/torch.nn.utils.prune.l1_unstructured.html#torch.nn.utils.prune.l1_unstructured) method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTz0Ae1cmjXy"
      },
      "source": [
        "def prune_l1_unstructured(model,prune_percent_weight,prune_percent_bias = 0):\n",
        "    ####################################################################\n",
        "    # Fill in all missing code below (...),\n",
        "    # then remove or comment the line below to test your function\n",
        "    raise NotImplementedError(\"Define the calculate inference function\")\n",
        "    ####################################################################\n",
        "\n",
        "    for name, module in trained_model.named_modules():\n",
        "        if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear):\n",
        "            #Prune both weight and bias using the prune_percent_weight and prune_percent_bias\n",
        "            ...\n",
        "            ...\n",
        "\n",
        "            print(\n",
        "                \"Sparsity in {}: {:.2f}%\".format(name,\n",
        "                    100. * float(torch.sum(module.weight == 0))\n",
        "                    / float(module.weight.nelement())\n",
        "                )\n",
        "            )\n",
        "##uncomment to run the test\n",
        "# test_model = Animal_Net()\n",
        "# prune_percent = 0.15\n",
        "# prune_l1_unstructured(test_model,0.15)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ixIimMveGNKT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14f032c4-2166-414c-872d-46c92486aaa0"
      },
      "source": [
        "def prune_l1_unstructured(model,prune_percent_weight,prune_percent_bias = 0):\n",
        "\n",
        "    for name, module in model.named_modules():\n",
        "        if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear):\n",
        "            prune.l1_unstructured(module, name='weight', amount=prune_percent_weight)\n",
        "            prune.l1_unstructured(module, name='bias', amount=prune_percent_bias)\n",
        "\n",
        "            print(\n",
        "                \"Sparsity in {}: {:.2f}%\".format(name,\n",
        "                    100. * float(torch.sum(module.weight == 0))\n",
        "                    / float(module.weight.nelement())\n",
        "                )\n",
        "            )\n",
        "\n",
        "##uncomment to run the test\n",
        "test_model = Animal_Net()\n",
        "prune_percent = 0.15\n",
        "prune_l1_unstructured(test_model,0.15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sparsity in fc1: 15.00%\n",
            "Sparsity in fc2: 14.99%\n",
            "Sparsity in fc3: 14.58%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BDpqNdJFqS5O"
      },
      "source": [
        "[Click for Solution](https://github.com/CIS-522/course-content/blob/main/tutorials/W05_Regularization/solutions/W5_Tutorial2_Ex02.py)\n",
        "\n",
        "Example Output:\n",
        "\n",
        "![Screenshot from 2021-02-12 18-52-30.png]()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2iNU0Uo5mIpC"
      },
      "source": [
        "In the section below, you will see the working of a very simple pruning technique which prunes a percentage of the parameters based on their weights."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OcROLun4YbeG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "2230290a-baee-42c4-90c7-c354a00f566e"
      },
      "source": [
        "args = {'test_batch_size': 1000,\n",
        "        'epochs': 200,\n",
        "        'lr': 5e-3,\n",
        "        'momentum': 0.9,\n",
        "        'no_cuda': False\n",
        "        }\n",
        "\n",
        "acc_dict = {}\n",
        "model = Big_Animal_Net()\n",
        "prune_percent = 0.5\n",
        "\n",
        "print(\"Training a randomly initialized model\")\n",
        "val_acc, train_acc, _, trained_model ,_ = main(args,model,train_loader,val_loader,img_test_dataset)\n",
        "\n",
        "##pruning a model\n",
        "print('Pruning and verifying and model:')\n",
        "prune_l1_unstructured(trained_model,prune_percent)\n",
        "\n",
        "#training the pruned model\n",
        "print(\"Training a pruned model\")\n",
        "val_acc_prune, train_acc_prune, _, pruned_model ,_ = main(args,trained_model.to('cpu'),train_loader,val_loader,img_test_dataset)\n",
        "\n",
        "val_acc_prune = [val_acc_prune[0]]*args['epochs'] + val_acc_prune\n",
        "train_acc_prune = [train_acc_prune[0]]*args['epochs'] + train_acc_prune\n",
        "plt.plot(val_acc,label='Val',c='blue',ls = 'dashed')\n",
        "plt.plot(train_acc,label='Train',c='blue',ls = 'solid')\n",
        "plt.plot(val_acc_prune,label='Val Prune',c='red',ls = 'dashed')\n",
        "plt.plot(train_acc_prune,label='Train Prune',c='red',ls = 'solid')\n",
        "plt.title('Pruning')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  0%|          | 1/200 [00:00<00:37,  5.33it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training a randomly initialized model\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 200/200 [00:32<00:00,  6.22it/s]\n",
            "  0%|          | 1/200 [00:00<00:31,  6.23it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Pruning and verifying and model:\n",
            "Sparsity in fc1: 50.00%\n",
            "Sparsity in fc2: 50.00%\n",
            "Sparsity in fc3: 50.00%\n",
            "Training a pruned model\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 200/200 [00:31<00:00,  6.28it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 579,
              "height": 459
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "720HNoTNrcLQ"
      },
      "source": [
        "Now change the prune_percent and report the percentage at which the model underfits."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQm_yEshrnMp",
        "cellView": "form"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "pruning_percent = '' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xyTIvCgsvZ3x"
      },
      "source": [
        "Let us say you create a new model with number of parameters equal to the number of parameters left after pruning. Do you think this model will work as good as the model which get after pruning the larger network?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fsr3vqmMvZ3z",
        "cellView": "form"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "under_parameterized_model = '' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8eoVO3_SUCa"
      },
      "source": [
        "In the above pruning technique after pruning the network, how do you think the performance of the will change if we re-initialize the weights while maintaing the prune mask?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOoExJGgTB_n",
        "cellView": "form"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "pruning_re_init = '' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qp3TtsUsZT_N"
      },
      "source": [
        "## Lottery Tickets\n",
        "(Time Estimate: 65 min from start)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jyFEk3srVf39"
      },
      "source": [
        "The lottery ticket hypothesis claims that \" A dense randomly initialized NN contains a subnetwork that is initialzed such that when trained in isolation it can match the test accuracy of the original network after training for at most same number of iterations\" i.e. a pruned model when reinitialized with the same weights will can match the test accuracy of the denser model. If the initialization changes the accuracy match is no longer guaranteed.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7qwr_pTEVVne"
      },
      "source": [
        "Here we train the following networks:\n",
        "\n",
        "\n",
        "1.   An unregularized model with Xavier initialization of weights for 200 epochs\n",
        "2.   A Pruned model with the weights reinitialized to the random values.\n",
        "3.   A pruned model with weights initialized with same Xavier Initialization as unregularized model.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Su8AFvyZMxsY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "outputId": "f5b66d5a-6e1d-4c78-d804-f20e895cf813"
      },
      "source": [
        "args = {'test_batch_size': 1000,\n",
        "        'epochs': 200,\n",
        "        'lr': 1e-3,\n",
        "        'momentum': 0.9,\n",
        "        'no_cuda': False,\n",
        "        }\n",
        "\n",
        "acc_dict = {}\n",
        "init_model = Big_Animal_Net()\n",
        "xavier_model = Big_Animal_Net()\n",
        "prune_percent = 0.4\n",
        "\n",
        "#Xavier Initilaization for one of the two models\n",
        "for name, module in xavier_model.named_modules():\n",
        "    if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear):\n",
        "        torch.nn.init.xavier_uniform_(module.weight)\n",
        "\n",
        "print('Training the full model')\n",
        "val_acc, train_acc, _, trained_model ,_ = main(args,copy.deepcopy(xavier_model),train_loader,val_loader,img_test_dataset)\n",
        "\n",
        "\n",
        "#prune the trained model\n",
        "prune_l1_unstructured(trained_model,prune_percent)\n",
        "\n",
        "#initialize masks for the initialzed model and xavier model\n",
        "for name, module in init_model.named_modules():\n",
        "    if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear):\n",
        "        prune.identity(module, name='weight')\n",
        "        prune.identity(module, name='bias')\n",
        "\n",
        "for name, module in xavier_model.named_modules():\n",
        "    if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear):\n",
        "        prune.identity(module, name='weight')\n",
        "        prune.identity(module, name='bias')\n",
        "\n",
        "init_modules = [[name,module] for name, module in init_model.named_modules()]\n",
        "xavier_modueles = [[name,module] for name, module in xavier_model.named_modules()]\n",
        "trained_modules = [[name,module] for name, module in trained_model.named_modules()]\n",
        "\n",
        "for i in range(len(init_modules)):\n",
        "    if isinstance(init_modules[i][1], torch.nn.Conv2d) or isinstance(init_modules[i][1], torch.nn.Linear):\n",
        "        init_modules[i][1].weight_mask = copy.deepcopy(trained_modules[i][1].weight_mask) \n",
        "        init_modules[i][1].bias_mask = copy.deepcopy(trained_modules[i][1].bias_mask)\n",
        "\n",
        "for i in range(len(xavier_modueles)):\n",
        "    if isinstance(xavier_modueles[i][1], torch.nn.Conv2d) or isinstance(xavier_modueles[i][1], torch.nn.Linear):\n",
        "        xavier_modueles[i][1].weight_mask = copy.deepcopy(trained_modules[i][1].weight_mask) \n",
        "        xavier_modueles[i][1].bias_mask = copy.deepcopy(trained_modules[i][1].bias_mask)\n",
        "\n",
        "\n",
        "print('Training the pruned and Xavier model')\n",
        "val_acc_lottery_x, train_acc_lottery_x, _, pruned_model_x ,_ = main(args,xavier_model,train_loader,val_loader,img_test_dataset)\n",
        "print('Training the pruned Init model')\n",
        "val_acc_lottery, train_acc_lottery, _, pruned_model ,_ = main(args,init_model,train_loader,val_loader,img_test_dataset)\n",
        "\n",
        "plt.plot(val_acc,c='blue',ls = 'dashed')\n",
        "plt.plot(train_acc,label='Train - full model',c='blue',ls = 'solid')\n",
        "plt.plot(val_acc_lottery,c='red',ls = 'dashed')\n",
        "plt.plot(train_acc_lottery,label='Train - Random',c='red',ls = 'solid')\n",
        "plt.plot(val_acc_lottery_x,c='green',ls = 'dashed')\n",
        "plt.plot(train_acc_lottery_x,label='Train - Xavier',c='green',ls = 'solid')\n",
        "plt.title('Lottery Tickets')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  0%|          | 1/200 [00:00<00:30,  6.59it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training the full model\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 200/200 [00:31<00:00,  6.35it/s]\n",
            "  0%|          | 1/200 [00:00<00:31,  6.36it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Sparsity in fc1: 40.00%\n",
            "Sparsity in fc2: 39.99%\n",
            "Sparsity in fc3: 40.10%\n",
            "Training the pruned and Xavier model\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 200/200 [00:31<00:00,  6.33it/s]\n",
            "  0%|          | 1/200 [00:00<00:30,  6.51it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training the pruned Init model\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 200/200 [00:31<00:00,  6.36it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 579,
              "height": 459
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EYTqX4Xv0DNn"
      },
      "source": [
        "Why according to you does the first train epoch of lottery ticket have such a high train accuracy?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8LCQbv80DNt",
        "cellView": "form"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "lottery_tickets = 'value' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUtwddoZ9J4P"
      },
      "source": [
        "#Section3: Distillation\n",
        "(Time Estimate: 90 min from start)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0JPlxMQOpVmE"
      },
      "source": [
        "Bigger neural nets are better for model performance but require significant memory, while smaller networks tend be be less accurate but are easier to deploy and use. \n",
        "\n",
        "Distillation is a technique which allows us to train smaller networks such that they mimic the outputs of the bigger network. The bigger network is called the teacher network wheras the smaller one is the student network. \n",
        "\n",
        "Distillation begins by training a teacher network. It then trains the student network with both the original labels and \"soft\" labels--the output of the teacher model. This lets us train the student network on unlabelled datasets, using the labeled given by the teacher network. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0cLGyqf8oUd",
        "cellView": "form"
      },
      "source": [
        "#@title Video : Distillation\n",
        "try: t4;\n",
        "except NameError: t4=time.time()\n",
        "\n",
        "from IPython.display import YouTubeVideo\n",
        "video = YouTubeVideo(id=\"y_KzDpklMmE\", width=854, height=480, fs=1)\n",
        "print(\"Video available at https://youtube.com/watch?v=\" + video.id)\n",
        "video"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FX6oKO3oDS5e"
      },
      "source": [
        "Let's begin by desiging a smaller network and training the parent model and also the small model using hard labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHA10EuJrmjV"
      },
      "source": [
        "class Small_Animal_Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        torch.manual_seed(32)\n",
        "        super(Small_Animal_Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(3*32*32, 32)\n",
        "        self.fc2 = nn.Linear(32, 3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(x.shape[0],-1)\n",
        "        x = F.leaky_relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        output = F.log_softmax(x, dim=1)\n",
        "        return output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-zsV74w3P8m",
        "cellView": "form"
      },
      "source": [
        "#@title Train the models (Run Me!)\n",
        "args = {'test_batch_size': 1000,\n",
        "        'epochs': 200,\n",
        "        'momentum': 0.9,\n",
        "        'no_cuda': False,\n",
        "        'lr' : 5e-3,\n",
        "        'cross_entropy':True\n",
        "        }\n",
        "\n",
        "Bmodel = Big_Animal_Net()\n",
        "Smodel = Small_Animal_Net()\n",
        "\n",
        "val_acc_big, train_acc_big, _, trained_big_model ,_ = main(args,Bmodel,train_loader,val_loader,img_test_dataset)\n",
        "val_acc_small, train_acc_small, _, _ ,_ = main(args,Smodel,train_loader,val_loader,img_test_dataset)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6b8BnQzOrb7t"
      },
      "source": [
        "Loss Function:\n",
        "\n",
        "We use the same cross entropy loss as before but we use both hard and soft labels to calculate loss. We cannot directly use the CrossEntropy Loss in Pytorch to calculate loss for soft labels. Hence we will exploit the relation between Cross Entropy and KL Divergence. \n",
        "\n",
        "Cross Entropy loss and KL Divergence are both related by: H(p,q) = H(p) + KL(p,q) where H(p,q) calculates cross entropy loss between distributions p and q wheras KL represents KL divergence. Here p is the probability distribution of soft_outputs which are constant and hence we can omit from the loss function.\n",
        "\n",
        "        L = (1 - alpha)*CE(outputs,ground_truth) + alpha * (T**2) *CE(outputs,soft_targets)\n",
        "        L = (1 - alpha)*CE(outputs,ground_truth) + alpha * (T**2) *KL(outputs,soft_targets)\n",
        "\n",
        "Here alpha and temperature are hyper parameters where temperatures is used to smoothen the outputs of the parent network. \n",
        "\n",
        "[Click to learn more about the relation](https://adventuresinmachinelearning.com/cross-entropy-kl-divergence/)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKhrkVwY6Wlq"
      },
      "source": [
        "def distillation_loss(args,soft_outputs, pred_logits, target):\n",
        "\n",
        "    alpha = args['alpha']\n",
        "    T = args['temperature']\n",
        "    dist_loss = (1. - alpha) * F.cross_entropy(pred_logits, target) + \\\n",
        "                    (alpha * (T ** 2)) * F.kl_div(F.log_softmax(pred_logits/T, dim=1),\n",
        "                             F.softmax(soft_outputs/T, dim=1))\n",
        "\n",
        "    return dist_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dH5qZx-HQcDT",
        "cellView": "form"
      },
      "source": [
        "#@title Modified Train Functions (Run Me!)\n",
        "def train_softmax_distillation(args, student_model,parent_model, device, train_loader, optimizer, epoch):\n",
        "    \n",
        "    student_model.train()\n",
        "    parent_model.eval()\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        data, target = data.to(device), target.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        soft_outputs = parent_model(data)\n",
        "        pred_logits = student_model(data)\n",
        "        loss = distillation_loss(args,soft_outputs.detach(), pred_logits, target)\n",
        "        loss.backward()\n",
        "        optimizer.step()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mXaMJOfVP0TL",
        "cellView": "form"
      },
      "source": [
        "#@title Modified Main Function (Run Me!)\n",
        "def distilation_main(args, teacher_model,student_model,train_loader,val_loader):\n",
        "\n",
        "    use_cuda = not args['no_cuda'] and torch.cuda.is_available()\n",
        "    device = torch.device('cuda' if use_cuda else 'cpu')\n",
        "\n",
        "    student_model = student_model.to(device)\n",
        "    teacher_model = teacher_model.to(device)\n",
        "    optimizer = optim.SGD(student_model.parameters(), lr=args['lr'], momentum=args['momentum'])\n",
        "    val_acc_list = []\n",
        "    train_acc_list = []\n",
        "    for epoch in tqdm(range(1, args['epochs'] + 1)):\n",
        "        train_softmax_distillation(args, student_model,teacher_model, device, train_loader, optimizer, epoch)\n",
        "        train_acc = test(student_model,device,train_loader,'Train')\n",
        "        val_acc = test(student_model,device,val_loader,'Val')\n",
        "        val_acc_list.append(val_acc)\n",
        "        train_acc_list.append(train_acc)\n",
        "\n",
        "    return val_acc_list, train_acc_list, student_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o13VleaKFAtE"
      },
      "source": [
        "Now that we have everything ready let's train the student network."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rp-5pGaeubLc"
      },
      "source": [
        "args = {'test_batch_size': 1000,\n",
        "        'epochs': 200,\n",
        "        'momentum': 0.9,\n",
        "        'no_cuda': False,\n",
        "        'lr' : 5e-3,\n",
        "        'alpha': 1,\n",
        "        'temperature': 40\n",
        "        }\n",
        "\n",
        "student_model = Small_Animal_Net()\n",
        "\n",
        "val_acc_st, train_acc_st, _, = distilation_main(args,trained_big_model,student_model,train_loader,val_loader)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtRFEj_Uwq1l",
        "cellView": "form"
      },
      "source": [
        "#@title Plot the validation curves of small model trained on hard and soft labels \n",
        "plt.plot(val_acc_small,label='Val - Small Model',c='red',ls = 'dashed')\n",
        "plt.axhline(y = max(val_acc_small),c = 'red')\n",
        "plt.plot(val_acc_st,label='Val - Student Model',c='green',ls = 'dashed')\n",
        "plt.axhline(y = max(val_acc_st),c = 'green')\n",
        "plt.title('Distillation')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZXhCP6P5Snd_"
      },
      "source": [
        "This method not only provides a way to train small and better networks but also gives us a chance to train small networks on more data using the soft labels from the heavily optimized parent networks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zUu5orrg0UHc"
      },
      "source": [
        "What other techniques can you use to reduce the dimensionality or size of the big network or the input data?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayCEX2zp0UHl",
        "cellView": "form"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "distillation = 'value' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8SsdI6tVz96F"
      },
      "source": [
        "Do you think regularization helps when you have infinite data available?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rLGFyIPz96J",
        "cellView": "form"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "data = 'value' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJZMeMTm1NG6"
      },
      "source": [
        "Which regualarization technique from this week do you think had the biggest effect on the network and why do you think so? Can you apply all of them on the same network?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2d99_VOK1NHG",
        "cellView": "form"
      },
      "source": [
        "#@markdown Write down your discussion\n",
        "complete = 'value' #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5HDVVEljKGQ"
      },
      "source": [
        "# Section 4: Adversarial  Attacks\n",
        "Time Estimate: (115 minutes from start)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03YLRgGemuN6",
        "cellView": "form"
      },
      "source": [
        "#@title Video : Adversarial\n",
        "try: t5;\n",
        "except NameError: t5=time.time()\n",
        "\n",
        "from IPython.display import YouTubeVideo\n",
        "video = YouTubeVideo(id=\"2e-PxxlGfpM\", width=854, height=480, fs=1)\n",
        "print(\"Video available at https://youtube.com/watch?v=\" + video.id)\n",
        "video"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FcaFjyzR4aJo"
      },
      "source": [
        "Designing perturbations to the input data to trick any machine learning model is called Adversarial attacks. These attacks are an inevitable consequence of learning in high dimensional search space and complex decision boundaries. Depending on the application these attacks can be very dangerous.\n",
        "\n",
        "![Adversarial Examples of a Stop Sign](https://media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs13638-020-01775-5/MediaObjects/13638_2020_1775_Fig1_HTML.png?as=webp)\n",
        "\n",
        "Hence, it is important for us to build models which can defend against such attcks. One possible way to do it is by reducing the dimensionality of the network which is also a byproduct of good regularization. A few ways of building models robust to such attachs are:\n",
        "\n",
        "\n",
        "\n",
        "*   [Defensive Distillation](https://deepai.org/machine-learning-glossary-and-terms/defensive-distillation) : Models trained via distillation are less prone to such attacks as they are trained on soft labels as there is an element of randomness in the training process.\n",
        "*   [Feature Squeezing](https://evademl.org/squeezing/): Identifies adversevial attacks for on-line classifiers whose model is being used by comparing model's perdiction before and after squeezing the input. \n",
        "* [SGD](https://arxiv.org/abs/1706.06083) You can also pick weight to minimize what the adversary is trying to maximize via SGD.\n",
        "\n",
        "In this weeks HW you will be designing an attack and also defending your model against it using regularization techniques you learned this week. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VtRxB698CTfG"
      },
      "source": [
        "---\n",
        "# Wrap up"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P5-HZSWcCbr3"
      },
      "source": [
        "## Submit responses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCJJf7OFk8SU",
        "cellView": "form"
      },
      "source": [
        "#@markdown #Run Cell to Show Airtable Form\n",
        "#@markdown ##**Confirm your answers and then click \"Submit\"**\n",
        "\n",
        "import time\n",
        "import numpy as np\n",
        "import urllib.parse\n",
        "from IPython.display import IFrame\n",
        "def prefill_form(src, fields: dict):\n",
        "  '''\n",
        "  src: the original src url to embed the form\n",
        "  fields: a dictionary of field:value pairs,\n",
        "  e.g. {\"pennkey\": my_pennkey, \"location\": my_location}\n",
        "  '''\n",
        "  prefill_fields = {}\n",
        "  for key in fields:\n",
        "      new_key = 'prefill_' + key\n",
        "      prefill_fields[new_key] = fields[key]\n",
        "  prefills = urllib.parse.urlencode(prefill_fields)\n",
        "  src = src + prefills\n",
        "  return src\n",
        "\n",
        "\n",
        "#autofill time if it is not present\n",
        "try: t0;\n",
        "except NameError: t0 = time.time()\n",
        "try: t1;\n",
        "except NameError: t1 = time.time()\n",
        "try: t2;\n",
        "except NameError: t2 = time.time()\n",
        "try: t3;\n",
        "except NameError: t3 = time.time()\n",
        "try: t4;\n",
        "except NameError: t4 = time.time()\n",
        "try: t5;\n",
        "except NameError: t5 = time.time()\n",
        "\n",
        "#autofill fields if they are not present\n",
        "#a missing pennkey and pod will result in an Airtable warning\n",
        "#which is easily fixed user-side.\n",
        "try: my_pennkey;\n",
        "except NameError: my_pennkey = \"\"\n",
        "try: my_pod;\n",
        "except NameError: my_pod = \"\"\n",
        "try: question_of_the_week;\n",
        "except NameError: question_of_the_week = \"\"\n",
        "try: learning_rate;\n",
        "except NameError: learning_rate = \"\"\n",
        "try: batch_size;\n",
        "except NameError: batch_size = \"\"\n",
        "try: number_of_parameters;\n",
        "except NameError: number_of_parameters = \"\"\n",
        "try: pruning_percent;\n",
        "except NameError: pruning_percent = \"\"\n",
        "try: under_parameterized_model;\n",
        "except NameError: under_parameterized_model = \"\"\n",
        "try: pruning_re_init;\n",
        "except NameError: pruning_re_init = \"\"\n",
        "try: lottery_tickets;\n",
        "except NameError: lottery_tickets = \"\"\n",
        "try: distillation;\n",
        "except NameError: distillation = \"\"\n",
        "try: cumilative_times;\n",
        "except NameError: cumilative_times = \"\"\n",
        "try: complete;\n",
        "except NameError: complete = \"\"\n",
        "try: data;\n",
        "except NameError: data = \"\"\n",
        "\n",
        "times = [(t-t0) for t in [t1,t2,t3,t4,t5]]\n",
        "\n",
        "fields = {\"my_pennkey\": my_pennkey,\n",
        "          \"my_pod\": my_pod,\n",
        "          \"question_of_the_week\":question_of_the_week,\n",
        "          \"learning_rate\":learning_rate,\n",
        "          \"batch_size\":batch_size,\n",
        "          \"number_of_parameters\":number_of_parameters,\n",
        "          \"pruning_percent\":pruning_percent,\n",
        "          \"under_parameterized_model\":under_parameterized_model,\n",
        "          \"pruning_re_init\":pruning_re_init,\n",
        "          \"lottery_tickets\": lottery_tickets,\n",
        "          \"distillation\": complete,\n",
        "          \"cumilative_times\": times\n",
        "        }\n",
        "\n",
        "src=\"https://airtable.com/embed/shr2XivycAmugUavT?\"\n",
        "\n",
        "#now instead of the original source url, we do: src = prefill_form(src, fields)\n",
        "display(IFrame(src = prefill_form(src, fields), width = 800, height = 400))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HKn5d3CCC05w"
      },
      "source": [
        "## Feedback\n",
        "How could this session have been better? How happy are you in your group? How do you feel right now?\n",
        "\n",
        "Feel free to use the embeded form below or use this link:\n",
        "<a target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://airtable.com/shrNSJ5ECXhNhsYss\">https://airtable.com/shrNSJ5ECXhNhsYss</a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIvhG6VZ8zez"
      },
      "source": [
        "display(IFrame(src=\"https://airtable.com/embed/shrNSJ5ECXhNhsYss?backgroundColor=red\", width = 800, height = 400))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WcXYkb0vDvl"
      },
      "source": [
        "## Homeworks\n",
        "\n",
        "1.   [Understanding Generalization](https://docs.google.com/document/d/1XOaTXYBleQlDNFM1-t512RHfJXRwA4-LIejuBA6pbLY/edit)\n",
        "2.   [Adversarial Attacks](https://)"
      ]
    }
  ]
}